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With respect to variable selection for linear regression models, this paper proposes a novel boosting learning method based on genetic algorithm. Its main idea is as follows: each training example is first assigned to a weight and genetic algorithm is adopted as the base learning algorithm of boosting. Then, the training set associated with a weight distribution is taken as the input of genetic algorithm...
In silicon testing, a Shmoo plot is commonly used to give us an insight into the silicon manufacturing development health. Shmoo plots and other silicon characterization data has high value, however, analysis of them is a time-consuming work. This paper establishes a machine learning based model to improve and automate the procedure in silicon data analysis for HVM test content development. Our experiment...
Traditional Web spam classifiers use only labeled data (feature/label pairs) to train. Labeled spam instances, however, are very difficult, expensive, or time consuming to obtain, as they require the efforts of experienced human annotators. Meanwhile unlabeled samples are relatively easy to collect. Semi-supervised learning addresses the classification problem by using large amount of unlabeled data,...
The current study presented a generalized regression neural network (GRNN) based approach to predict nitrogen oxides (NOx) emitted from coal-fired boiler. A novel 'multiple' smoothing parameters, which is different from the standard algorithm in which only single smoothing parameter was adopted (Matlab neural network toolbox, for example), were assigned to GRNN model. K-means clustering algorithm...
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